Knowledge base inference using bridging entities

Abstract

Large-scale Knowledge Bases (such as NELL, Yago, Freebase, etc.) are often sparse, i.e., a large number of valid relations between existing entities are missing. Recent research have addressed this problem by augmenting the KB graph with additional edges mined from a large text corpus while keeping the set of nodes fixed, and then using the Path Ranking Algorithm (PRA) to perform KB inference over this augmented graph. In this paper, we extend this line of work by augmenting the KB graph not only with edges, but also with bridging entities, where both the edges and bridging entities are mined from a 500 million web text corpus. Through experiments on real-world datasets, we demonstrate the value of bridging entities in improving the performance and running time of PRA in the KB inference task.